${\mathit{MALT^P}}$ : Parallel Prediction of Malicious Tweets

It has been reported that embedded URLs and multimodal content (images, video, and sound recordings) in tweets are increasingly used to seduce users into a “wrong click,” leading to malware infection. In this paper, we predict whether a tweet is malicious or not by examining five classes of features: textual content including sentiment, paths emanating from a URL mentioned in the tweet, attributes associated with URLs, and multimodal content in the tweet. A fifth class of features first constructs a novel “tweet graph” and then defines features by analyzing “metapaths” contained in the tweet graph. Next, we propose a MALicious Tweets in Parallel (
${\mathit {MALT^{P}}}$
) collective classification algorithm that merges together tweet graphs, metapaths, and collective classification proposed previously in the literature. We conduct detailed experiments using two data sets—Warningbird (WB) and KBA. We show that our metapath-based approach outperforms past efforts at identifying malicious tweets and further show that metapath-based features in conjunction with Alexa ranks and features from KBA yield very high predictive accuracy—over 0.98 on KBA and over 0.94 on KBA, outperforming past work. More significantly, metapath features alone generate a predictive accuracy of 0.977 and 0.923, respectively, on the KBA and WB data sets, significantly outperforming the other methods in isolation. We conduct a further analysis to identify the most important features; surprisingly, our results show that the presence of multimodal content is not a major factor and that metapath-based features dominate in separating malicious from benign tweets.